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📄 237,365% (2,373.65x) speedup for sorter in code_to_optimize/bubble_sort.py

⏱️ Runtime : 1.25 second 525 microseconds (best of 929 runs)

📝 Explanation and details

Certainly! The given program is using a bubble sort algorithm, which has a time complexity of O(n^2). An optimized algorithm for sorting is the Timsort (used in Python's in-built sort), which has a time complexity of O(n log n). Let's rewrite the code using Python's in-built sorting function.

The resulting functionality remains the same, but the performance will be significantly better for larger lists due to the optimized sorting algorithm.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 53 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests Details
import pytest  # used for our unit tests
from code_to_optimize.bubble_sort import sorter

# unit tests

# Basic Functionality Tests
def test_simple_unsorted_list():
    codeflash_output = sorter([3, 1, 2])
    codeflash_output = sorter([5, 3, 4, 1, 2])

def test_already_sorted_list():
    codeflash_output = sorter([1, 2, 3])
    codeflash_output = sorter([1, 2, 3, 4, 5])

def test_reverse_sorted_list():
    codeflash_output = sorter([3, 2, 1])
    codeflash_output = sorter([5, 4, 3, 2, 1])

# Edge Case Tests
def test_empty_list():
    codeflash_output = sorter([])

def test_single_element_list():
    codeflash_output = sorter([1])
    codeflash_output = sorter([42])

def test_two_elements_list():
    codeflash_output = sorter([2, 1])
    codeflash_output = sorter([1, 2])

# Lists with Duplicate Elements
def test_all_elements_same():
    codeflash_output = sorter([1, 1, 1])
    codeflash_output = sorter([5, 5, 5, 5, 5])

def test_some_duplicate_elements():
    codeflash_output = sorter([3, 1, 2, 1, 3])
    codeflash_output = sorter([4, 5, 4, 3, 2, 2])

# Lists with Negative Numbers
def test_negative_and_positive_numbers():
    codeflash_output = sorter([-1, 2, -3, 4])
    codeflash_output = sorter([3, -2, -1, 4, 0])

def test_all_negative_numbers():
    codeflash_output = sorter([-3, -1, -2])
    codeflash_output = sorter([-5, -4, -3, -2, -1])

# Lists with Mixed Data Types (if applicable)
def test_integers_and_floats():
    codeflash_output = sorter([1, 2.5, 3, 0.5])
    codeflash_output = sorter([3.1, 2.2, 1.3, 4.4])

def test_strings_and_integers():
    with pytest.raises(TypeError):
        sorter([1, "two", 3])
    with pytest.raises(TypeError):
        sorter(["one", 2, "three"])

# Large Scale Test Cases
def test_large_list_of_random_numbers():
    codeflash_output = sorter(list(range(1000, 0, -1)))
    codeflash_output = sorter(list(range(10000, 9000, -1)))

def test_large_list_with_duplicates():
    codeflash_output = sorter([1, 2] * 500)
    codeflash_output = sorter([3, 3, 2, 2, 1, 1] * 1000)

# Performance and Scalability
def test_performance_large_sorted_list():
    codeflash_output = sorter(list(range(1000)))

def test_performance_large_reverse_sorted_list():
    codeflash_output = sorter(list(range(100000, 99000, -1)))
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest  # used for our unit tests
from code_to_optimize.bubble_sort import sorter

# unit tests

def test_empty_list():
    # Test sorting an empty list
    codeflash_output = sorter([])

def test_single_element_list():
    # Test sorting a list with a single element
    codeflash_output = sorter([1])
    codeflash_output = sorter(["a"])

def test_already_sorted_list():
    # Test sorting an already sorted list
    codeflash_output = sorter([1, 2, 3, 4, 5])
    codeflash_output = sorter(["a", "b", "c", "d"])

def test_reverse_sorted_list():
    # Test sorting a reverse sorted list
    codeflash_output = sorter([5, 4, 3, 2, 1])
    codeflash_output = sorter(["d", "c", "b", "a"])

def test_unsorted_list_with_unique_elements():
    # Test sorting an unsorted list with unique elements
    codeflash_output = sorter([3, 1, 4, 2, 5])
    codeflash_output = sorter(["b", "d", "a", "c"])

def test_unsorted_list_with_duplicate_elements():
    # Test sorting an unsorted list with duplicate elements
    codeflash_output = sorter([3, 1, 4, 2, 5, 3, 1])
    codeflash_output = sorter(["b", "d", "a", "c", "b", "a"])

def test_list_with_all_identical_elements():
    # Test sorting a list with all identical elements
    codeflash_output = sorter([1, 1, 1, 1])
    codeflash_output = sorter(["a", "a", "a", "a"])

def test_list_with_negative_numbers():
    # Test sorting a list with negative numbers
    codeflash_output = sorter([-3, -1, -4, -2, -5])
    codeflash_output = sorter([3, -1, 4, -2, 5])

def test_list_with_mixed_data_types():
    # Test sorting a list with mixed data types (if applicable)
    with pytest.raises(TypeError):
        sorter([3, "a", 2, "b"])

def test_large_list():
    # Test sorting a large list to assess performance and scalability
    large_list = list(range(1000, 0, -1))
    sorted_large_list = list(range(1, 1001))
    codeflash_output = sorter(large_list)

def test_list_with_floating_point_numbers():
    # Test sorting a list with floating point numbers
    codeflash_output = sorter([3.1, 2.4, 5.6, 1.2, 4.8])
    codeflash_output = sorter([1.1, 1.2, 1.3, 1.1])

def test_list_with_special_characters():
    # Test sorting a list with special characters
    codeflash_output = sorter(["!", "@", "#", "$", "%"])
    codeflash_output = sorter(["a", "!", "b", "@", "c"])

def test_list_with_mixed_case_strings():
    # Test sorting a list with mixed case strings
    codeflash_output = sorter(["a", "B", "c", "A", "b"])
    codeflash_output = sorter(["apple", "Banana", "cherry", "Apple", "banana"])

def test_list_with_none_values():
    # Test sorting a list with None values (if applicable)
    with pytest.raises(TypeError):
        sorter([3, None, 2, None, 1])
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-sorter-m8qddccu and push.

Codeflash

Certainly! The given program is using a bubble sort algorithm, which has a time complexity of O(n^2). An optimized algorithm for sorting is the Timsort (used in Python's in-built sort), which has a time complexity of O(n log n). Let's rewrite the code using Python's in-built sorting function.



The resulting functionality remains the same, but the performance will be significantly better for larger lists due to the optimized sorting algorithm.
@codeflash-ai-dev codeflash-ai-dev bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Mar 26, 2025
@codeflash-ai-dev codeflash-ai-dev bot requested a review from alvin-r March 26, 2025 20:20
@alvin-r alvin-r closed this Mar 26, 2025
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